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From the course by University of Maryland, College Park

Dealing With Missing Data

67 ratings

University of Maryland, College Park

Dealing With Missing Data

67 ratings

Course 5 of 7 in the Specialization Survey Data Collection and Analytics

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®.

From the lesson

Implementing the Steps

Software is critical to implementing the steps, but the R system is an excellent source of free routines. This module covers several R packages, including sampling, survey, and PracTools that will select samples and compute weights.

Meet the Instructors

Richard Valliant, Ph.D.
Research Professor
Joint Program in Survey Methodology

Now that you've learned about the general steps in waiting,

what we'll do in module 3 is move on to how you actually do those.

And I'll give you a number of software examples.

So I want to start out with an overview of the software that's available.

Now the one we're going to emphasize in this course is R and

some packages that are available within R.

So the great thing about R is that it's free.

Now the price is right and you can download it at this website,

r-project.org/.

That includes both the basic packages and

the add on packages that users have written.

So as of this spring 2016, there were over

8,000 user written R packages that are out there

on the CRAN Comprehensive R archive network that for

free download and they'll do a lot of really useful things.

We're going to be using three of them in this course.

One is called sampling.

Another one is called survey and another is called PracTools.

So we'll show you examples using all those.

Now how do you edit code files in R?

There's some options there.

One that is very popular and good is called R-Studio.

Another one is the share package called WinEdt

which is pretty common because you can also edit

LaTeX files in WinEdt and compile them.

In order to get the most good out of WinEdt,

you should download the RWinEdt (R package),

which causes WinEdt and R to be closely linked together.

You can highlight code in your editor window,

send it directly to the R console for execution.

And R-Studio does that too.

Tinn-R is another one that's out there.

It's also a Shareware package and it's got some of the same capabilities.

So, you can decide which one you like best and make a choice.

Now, there are other commercial software package that are available.

SAS is a well-known very large operation.

It has some survey capabilities.

Stata is another good package that's commercial.

It has got more capabilities than SAS.

In fact, it's got quite a lot of analytic capabilities that you won't find in

other packages.

Many of them built off of econometric modeling routines.

Another good package is SUDAAN.

The usual way that that's used is in conjunction with SAS.

So you, most conveniently, will have to have a SAS license and

a SUDAAN license in order to do this.

WesVar is a free piece of software that is built around replication,

variance estimation, and it's written by Westat,

which is a commercial company in the Washington, D.C. area.

It's available for download for free at the westat.com website.

Now, we're going to be doing a lot of our examples in the coming videos and

many of those are based on this Springer text that I've listed here.

Practical Tools for Designing and Weighing Survey Samples published in 2013.

So I happen to know this book well since I'm the first author.

This was written in conjunction with Jill Dever and

Frank Kreuter, who as you know by now is also co-instructor in this

Coursera sequence on survey methods and data collection.

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